CVE-2020-15209: Null Pointer Dereference in TensorFlow Lite
Summary
CVE-2020-15209: In tensorflow-lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, a crafted TFLite model can force a node to have as input a tensor backed by a nullptr buffer. Affects tensorflow-cpu across 6 version ranges.
< 0b5662bc2be13a8c8f044d925d87fb6e56247cd8
< 1.15.4
>= 2.0.0, < 2.0.3
>= 2.1.0, < 2.1.2
>= 2.2.0, < 2.2.1
>= 2.3.0, < 2.3.1
Fixed in 5 releases; the minimum fixed version depends on the release line you run. How to fix ↓
Also tracked for
- tensorflow · Bitnami
- tensorflow · PyPI
- tensorflow-gpu · PyPI
- tensorflow · PyPI
The same CVE-2020-15209 is tracked separately for each ecosystem and vendor. Its fix information is the same; the package and affected versions differ.
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Background
TensorFlow Lite is an open-source deep learning framework developed by Google. It is designed to run machine learning models on resource-constrained devices such as mobile phones and embedded systems. TensorFlow Lite provides a lightweight and efficient runtime for executing models in a variety of environments. However, versions before 1.15.4, 2.0.3, 2.1.2, 2.2.1, and 2.3.1 are vulnerable to a null pointer dereference issue.
Vulnerability Detail
The vulnerability in TensorFlow Lite arises from a flaw in the handling of TFLite models. A specially crafted TFLite model can manipulate the buffer index in the flatbuffer serialization, causing a node to have an input tensor backed by a nullptr buffer. This effectively converts a read-only tensor to a read-write one. The runtime assumes that these buffers are initialized before any possible read operation. However, since the model does not include any instructions to write to the manipulated tensor, a null pointer dereference occurs when attempting to access it. This vulnerability can be exploited to cause a denial-of-service condition or potentially execute arbitrary code.
How To Fix
Latest published version: 2.21.0 as of 22 Oct 2026
pip install --upgrade "tensorflow-cpu==2.21.0"Upgrading to it may include breaking changes; the versions below are the minimum fixed release for this vulnerability.
If you can't move to the latest
The minimum fixed version depends on the release line you run.
-
< 1.15.41.15.4pip install --upgrade "tensorflow-cpu==1.15.4" -
>= 2.0.0, < 2.0.32.0.3pip install --upgrade "tensorflow-cpu==2.0.3" -
>= 2.1.0, < 2.1.22.1.2pip install --upgrade "tensorflow-cpu==2.1.2" -
>= 2.2.0, < 2.2.12.2.1pip install --upgrade "tensorflow-cpu==2.2.1" -
>= 2.3.0, < 2.3.12.3.1pip install --upgrade "tensorflow-cpu==2.3.1"
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Workaround
There is no known workaround for this vulnerability. It is essential to update to the patched versions of TensorFlow Lite to mitigate the risk.
Conclusion
The null pointer dereference vulnerability in TensorFlow Lite can be exploited by a specially crafted TFLite model to cause a denial-of-service condition or potentially execute arbitrary code. It is crucial for users to update their installations to the patched versions of TensorFlow Lite (1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1) to mitigate the risk. For further support on vulnerability remediation, please contact DevNack.com.
See every vulnerability recorded for tensorflow-cpu →
FAQ
TensorFlow Lite is an open-source deep learning framework developed by Google. It is designed for running machine learning models on resource-constrained devices.
A null pointer dereference occurs when a program attempts to access or manipulate a memory location that has a null pointer value. This can lead to crashes or other unexpected behavior.
An attacker can craft a malicious TFLite model that triggers the null pointer dereference issue. By manipulating the buffer index in the model's flatbuffer serialization, they can convert a read-only tensor to a read-write one, causing a null pointer dereference when the tensor is accessed.
The vulnerability can lead to a denial-of-service condition or potentially allow an attacker to execute arbitrary code. This could result in the compromise of the affected system or the unauthorized access to sensitive information.
No, only versions before 1.15.4, 2.0.3, 2.1.2, 2.2.1, and 2.3.1 are vulnerable to this issue. It is recommended to update to the patched versions to mitigate the risk.
References
Related vulnerabilities
Recent vulnerabilities recorded for tensorflow-cpu.
